Method and apparatus for evaluating trigger phrase enrollment

ABSTRACT

An electronic device includes a microphone that receives an audio signal that includes a spoken trigger phrase, and a processor that is electrically coupled to the microphone. The processor measures characteristics of the audio signal, and determines, based on the measured characteristics, whether the spoken trigger phrase is acceptable for trigger phrase model training. If the spoken trigger phrase is determined not to be acceptable for trigger phrase model training, the processor rejects the trigger phrase for trigger phrase model training.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.15/609,342, filed May 31, 2017, which is a continuation of U.S.application Ser. No. 15/605,565, filed May 25, 2017, which is acontinuation of U.S. application Ser. No. 15/384,142, filed Dec. 19,2016, which is a continuation of U.S. application Ser. No. 14/050,596,filed Oct. 10, 2013, which claims the benefit of U.S. ProvisionalApplication No. 61/860,730 filed Jul. 31, 2013, which are incorporatedherein by reference.

TECHNICAL FIELD

The present disclosure relates to trigger phrase enrollment and, moreparticularly, to methods and devices for evaluating trigger phraseenrollment for trigger phrase training.

BACKGROUND

Although speech recognition has been around for decades, the quality ofspeech recognition software and hardware has only recently reached ahigh enough level to appeal to a large number of consumers. One area inwhich speech recognition has become very popular in recent years is thesmartphone and tablet computer industry. Using a speechrecognition-enabled device, a consumer can perform such tasks as makingphone calls, writing emails, and navigating with GPS using only voicecommands.

Speech recognition in such devices is far from perfect, however. Whenusing a speech recognition-enabled device for the first time, the usermay need to “train” the speech recognition software to recognize his orher voice. Even after training, however, the speech recognitionfunctions may not work well in all sound environments. For example, thepresence of background noise can decrease speech recognition accuracy.

In an always-on audio (AOA) system, a speech recognition-enabled devicecontinuously listens for the occurrence of a trigger phrase, which isalso referred to as a “hotword”. The trigger phrase, when detected,alerts the device that the user is about to issue a voice command or asequence of voice commands, which are then processed by a speechrecognition engine in the device. The system, by continuously listeningfor the occurrence of a trigger phrase, frees the user from having tomanually signal to the device that the voice command mode is beingentered, eliminating the need for an action such as pressing a physicalbutton or a virtual button or control via the device touch screen.

In the AOA system, it is advantageous for the user to train the triggerphrase recognizer for the user's voice. This allows the trigger phraserecognizer to adapt the trigger phrase recognition models to the user'svoice, thus improving the trigger phrase recognizer accuracy, and alsoto employ speaker recognition to help reject the trigger phrase when itis spoken by a person other than the user. For these advantages to berealized the user must go through the enrollment process to adapt thetrigger phrase model to the user's voice. The enrollment process, in anexample, involves the user being prompted to say the trigger phrasemultiple times (e.g., three times), while being in an acoustically quietenvironment. The three utterances of the trigger phrase, captured by amicrophone in the device, are digitally sampled, and used for triggerphrase model training. For the training to yield high quality triggerphrase models tailored to the user's voice, the three instances of thetrigger phrase recordings, made by the user in the enrollment process,should ideally have low background noise level, which has preferablystationary (i.e., not fluctuating with respect to time) characteristics,and not include tongue clicks, device handling noise, or other spuriousnon-speech sounds, such as pops, or clicks. If the enrollment recordingsof the trigger phrase do not satisfy the above requirements, the triggerphrase models adapted to the user will be of poor quality, resulting indegraded trigger phrase recognition accuracy.

DRAWINGS

While the appended claims set forth the features of the presenttechniques with particularity, these techniques may be best understoodfrom the following detailed description taken in conjunction with theaccompanying drawings of which:

FIG. 1 shows a user speaking to an electronic device, which is depictedas a mobile device in the drawing.

FIG. 2 shows example components of the electronic device of FIG. 1.

FIG. 3 shows example modules of a processor of the electronic device ofFIG. 1.

FIGS. 4-9 show steps that may be carried out according to variousembodiments.

DESCRIPTION

The present disclosure sets forth a method and apparatus for evaluatingtrigger phrase enrollment for trigger phrase training.

In an embodiment, an electronic device includes a microphone thatreceives an audio signal that includes a spoken trigger phrase, and aprocessor that is electrically coupled to the microphone. The processormeasures characteristics of the recorded audio signal, and determines,based on the measured characteristics, whether the spoken trigger phraseis acceptable for trigger phrase model training. If the spoken triggerphrase is determined not to be acceptable for trigger phrase modeltraining, the processor rejects the trigger phrase for trigger phrasemodel training.

In another embodiment, an electronic device records an audio signalincluding a spoken trigger phrase. The device measures a backgroundnoise level in the audio signal, and compares the measured backgroundnoise level to a threshold level. Based on the comparison, the devicethen determines whether the spoken trigger phrase is acceptable fortrigger phrase model training. If the spoken trigger phrase isdetermined not to be acceptable for trigger phrase model training, thedevice rejects the spoken trigger phrase for trigger phrase modeltraining.

The device may determine whether the measured background noise level isgreater than the threshold level. If the measured background noise levelis determined to be greater than the threshold level, the devicedetermines that the spoken trigger phrase is not acceptable for triggerphrase model training. In an embodiment, the threshold level may beabout −50 dB.

In yet another embodiment, an electronic device records an audio signalincluding a spoken trigger phrase. The device estimates the length ofthe spoken trigger phrase in the audio signal, and determines whetherthe estimated length of the spoken trigger phrase is less than a lowertrigger phrase length threshold. If the estimated length of the spokentrigger phrase is less than the lower trigger phrase length threshold,the device rejects the spoken trigger phrase for trigger phrase modeltraining.

To estimate the length of the spoken trigger phrase, the device maycount the number of frames in the audio signal having voice activity. Inan embodiment, the lower trigger phrase length threshold may be about 70frames.

In still another embodiment, an electronic device records an audiosignal including a spoken trigger phrase. The device estimates thelength of the spoken trigger phrase in the audio signal, and determineswhether the estimated length of the spoken trigger phrase is greaterthan a higher trigger phrase length threshold. If the estimated lengthof the spoken trigger phrase is greater than the higher trigger phraselength threshold, the device rejects the spoken trigger phrase fortrigger phrase model training.

To estimate the length of the spoken trigger phrase, the device maycount the number of frames having voice activity in the audio signal. Inan embodiment, the higher trigger phrase length threshold may be about180 frames.

In an embodiment, an electronic device records an audio signal includinga spoken trigger phrase. The device measures a number of segments in theaudio signal having voice activity, and compares the measured number ofsegments to a threshold value. Based on the comparison, the devicedetermines whether the spoken trigger phrase is acceptable for triggerphrase model training. If the spoken trigger phrase is determined not tobe acceptable for trigger phrase model training, the device rejects thespoken trigger phrase for trigger phrase model training.

The device may determine whether the measured number of segments isgreater than the threshold value. If the measured number of segments isdetermined to be greater than the threshold value, the device determinesthat the spoken trigger phrase is not acceptable for trigger phrasemodel training. In an embodiment, the threshold value may be based on anoffline analysis of the trigger phrase.

In another embodiment, an electronic device records an audio signalincluding a spoken trigger phrase. The device measures the length of theshortest segment in the audio signal having voice activity, and comparesthe measured length of the shortest segment to a threshold value. Basedon the comparison, the device determines whether the spoken triggerphrase is acceptable for trigger phrase model training. If the spokentrigger phrase is determined not to be acceptable for trigger phrasemodel training, the device rejects the spoken trigger phrase for triggerphrase model training.

The device may determine whether the measured length of the shortestsegment is less than the threshold value. If the measured length of theshortest segment is determined to be less than the threshold value, thedevice determines that the spoken trigger phrase is not acceptable fortrigger phrase model training. In an embodiment, the threshold value maybe about 27 frames.

In yet another embodiment, an electronic device records an audio signalincluding a spoken trigger phrase. The device calculates a measure ofnoise variability of background noise for each frame in the audiosignal, and compares the measure of noise variability of backgroundnoise for each frame to a first threshold value. The device then countsthe number of frames in the audio signal for which the measure of noisevariability of the background noise is higher than the first thresholdvalue, and compares the counted number of frames to a second thresholdvalue. Based on the counted number of frames, the device determineswhether the spoken trigger phrase is acceptable for trigger phrase modeltraining. If the spoken trigger phrase is determined not to beacceptable for trigger phrase model training, the device rejects thespoken trigger phrase for trigger phrase model training.

The device may determine whether the counted number of frames is equalto or greater than the second threshold value. If the counted number offrames is determined to be equal to or greater than the second thresholdvalue, the device determines that the spoken trigger phrase is notacceptable for trigger phrase model training. In an embodiment, thefirst threshold value may be about 0.7, and the second threshold valuemay be about 20.

To calculate the measure of noise variability of the background noise,the device may use the following equation:

${{MNV} = {\frac{1}{{NC} \times {nb}}{\sum\limits_{k = 1}^{NC}{\sum\limits_{l = 1}^{nb}\frac{\left( {{{D\_ smooth}\left( {k,l} \right)} - {{D\_ smooth}{\_ low}\left( {k,l} \right)}} \right)}{\left( {{{D\_ smooth}{\_ high}\left( {k,l} \right)} - {{D\_ smooth}{\_ low}\left( {k,l} \right)}} \right)}}}}},$wherein MNV denotes the measure noise variability of the backgroundnoise in the audio signal, NC denotes a number of channels in the audiosignal, nb+1 denotes a number of contiguous noise frames in the audiosignal, k denotes a channel index, l denotes a look-back index,D_smooth(k, l) denotes a smoothed maximum dB difference of smoothedchannel noise, D_smooth_high(k, l) denotes a high boundary point thatrepresents noise exhibiting high variability, and D_smooth_low (k, l)denotes a low boundary point that represents noise exhibiting lowvariability. In an embodiment, MNV may be bounded between 0 and 1.

The embodiments described herein are usable in the context of always-onaudio (AOA). When using AOA, the device 102 (FIG. 1) is capable ofwaking up from a sleep mode upon receiving a trigger command (i.e., atrigger phrase) from a user. AOA places additional demands on devices,especially mobile devices. AOA is most effective when the device 102 isable to recognize the user's voice commands accurately and quickly.

Referring to FIG. 1, a user 104 provides voice input (or vocalizedinformation or speech) 106 that is received by a speechrecognition-enabled electronic device (“device”) 102 by way of amicrophone (or other sound receiver) 108. The device 102, which is amobile device in this example, includes a touch screen display 110 thatis able to display visual images and to receive or sense touch typeinputs as provided by way of a user's finger or other touch input devicesuch as a stylus. Notwithstanding the presence of the touch screendisplay 110, in the embodiment shown in FIG. 1, the device 102 also hasa number of discrete keys or buttons 112 that serve as input devices ofthe device. However, in other embodiments such keys or buttons (or anyparticular number of such keys or buttons) need not be present, and thetouch screen display 110 can serve as the primary or only user inputdevice.

Although FIG. 1 particularly shows the device 102 as including the touchscreen display 110 and keys or buttons 112, these features are onlyintended to be examples of components/features on the device 102, and inother embodiments the device 102 need not include one or more of thesefeatures and/or can include other features in addition to or instead ofthese features.

The device 102 is intended to be representative of a variety of devicesincluding, for example, cellular telephones, personal digital assistants(PDAs), smart phones, or other handheld or portable electronic devices.In alternate embodiments, the device can also be a headset (e.g., aBluetooth headset), MP3 player, battery-powered device, a watch device(e.g., a wristwatch) or other wearable device, radio, navigation device,laptop or notebook computer, netbook, pager, PMP (personal mediaplayer), DVR (digital video recorders), gaming device, camera, e-reader,e-book, tablet device, navigation device with video capable screen,multimedia docking station, or other device.

Embodiments of the present disclosure are intended to be applicable toany of a variety of electronic devices that are capable of or configuredto receive voice input or other sound inputs that are indicative orrepresentative of vocalized information.

FIG. 2 shows internal components of the device 102 of FIG. 1, inaccordance with an embodiment of the disclosure. As shown in FIG. 2, theinternal components 200 include one or more wireless transceivers 202, aprocessor 204 (e.g., a microprocessor, microcomputer,application-specific integrated circuit, etc.), a memory portion 206,one or more output devices 208, and one or more input devices 210. Theinternal components 200 can further include a component interface 212 toprovide a direct connection to auxiliary components or accessories foradditional or enhanced functionality. The internal components 200 mayalso include a power supply 214, such as a battery, for providing powerto the other internal components while enabling the mobile device to beportable. Further, the internal components 200 additionally include oneor more sensors 228. All of the internal components 200 can be coupledto one another, and in communication with one another, by way of one ormore internal communication links 232 (e.g., an internal bus).

Further, in the embodiment of FIG. 2, the wireless transceivers 202particularly include a cellular transceiver 203 and a Wi-Fi transceiver205. More particularly, the cellular transceiver 203 is configured toconduct cellular communications, such as 3G, 4G, 4G-LTE, vis-à-vis celltowers (not shown), albeit in other embodiments, the cellulartransceiver 203 can be configured to utilize any of a variety of othercellular-based communication technologies such as analog communications(using AMPS), digital communications (using CDMA, TDMA, GSM, iDEN, GPRS,EDGE, etc.), and/or next generation communications (using UMTS, WCDMA,LTE, IEEE 802.16, etc.) or variants thereof.

By contrast, the Wi-Fi transceiver 205 is a wireless local area network(WLAN) transceiver 205 configured to conduct Wi-Fi communications inaccordance with the IEEE 802.11 (a, b, g, or n) standard with accesspoints. In other embodiments, the Wi-Fi transceiver 205 can instead (orin addition) conduct other types of communications commonly understoodas being encompassed within Wi-Fi communications such as some types ofpeer-to-peer (e.g., Wi-Fi Peer-to-Peer) communications. Further, inother embodiments, the Wi-Fi transceiver 205 can be replaced orsupplemented with one or more other wireless transceivers configured fornon-cellular wireless communications including, for example, wirelesstransceivers employing ad hoc communication technologies such as HomeRF(radio frequency), Home Node B (3G femtocell), Bluetooth and/or otherwireless communication technologies such as infrared technology.

Although in the present embodiment the device 102 has two of thewireless transceivers 202 (that is, the transceivers 203 and 205), thepresent disclosure is intended to encompass numerous embodiments inwhich any arbitrary number of wireless transceivers employing anyarbitrary number of communication technologies are present. By virtue ofthe use of the wireless transceivers 202, the device 102 is capable ofcommunicating with any of a variety of other devices or systems (notshown) including, for example, other mobile devices, web servers, celltowers, access points, other remote devices, etc. Depending upon theembodiment or circumstance, wireless communication between the device102 and any arbitrary number of other devices or systems can beachieved.

Operation of the wireless transceivers 202 in conjunction with others ofthe internal components 200 of the device 102 can take a variety offorms. For example, operation of the wireless transceivers 202 canproceed in a manner in which, upon reception of wireless signals, theinternal components 200 detect communication signals and thetransceivers 202 demodulate the communication signals to recoverincoming information, such as voice and/or data, transmitted by thewireless signals. After receiving the incoming information from thetransceivers 202, the processor 204 formats the incoming information forthe one or more output devices 208. Likewise, for transmission ofwireless signals, the processor 204 formats outgoing information, whichcan but need not be activated by the input devices 210, and conveys theoutgoing information to one or more of the wireless transceivers 202 formodulation so as to provide modulated communication signals to betransmitted.

Depending upon the embodiment, the input and output devices 208, 210 ofthe internal components 200 can include a variety of visual, audioand/or mechanical outputs. For example, the output device(s) 208 caninclude one or more visual output devices 216 such as a liquid crystaldisplay and/or light emitting diode indicator, one or more audio outputdevices 218 such as a speaker, alarm, and/or buzzer, and/or one or moremechanical output devices 220 such as a vibrating mechanism. The visualoutput devices 216 among other things can also include a video screen.Likewise, by example, the input device(s) 210 can include one or morevisual input devices 222 such as an optical sensor (for example, acamera lens and photosensor), one or more audio input devices 224 suchas the microphone 108 of FIG. 1 (or further for example a microphone ofa Bluetooth headset), and/or one or more mechanical input devices 226such as a flip sensor, keyboard, keypad, selection button, navigationcluster, touch pad, capacitive sensor, motion sensor, and/or switch.Operations that can actuate one or more of the input devices 210 caninclude not only the physical pressing/actuation of buttons or otheractuators, but can also include, for example, opening the mobile device,unlocking the device, moving the device to actuate a motion, moving thedevice to actuate a location positioning system, and operating thedevice.

As mentioned above, the internal components 200 also can include one ormore of various types of sensors 228 as well as a sensor hub to manageone or more functions of the sensors. The sensors 228 may include, forexample, proximity sensors (e.g., a light detecting sensor, anultrasound transceiver or an infrared transceiver), touch sensors,altitude sensors, and one or more location circuits/components that caninclude, for example, a Global Positioning System (GPS) receiver, atriangulation receiver, an accelerometer, a tilt sensor, a gyroscope, orany other information collecting device that can identify a currentlocation or user-device interface (carry mode) of the device 102.Although the sensors 228 for the purposes of FIG. 2 are considered to bedistinct from the input devices 210, in other embodiments it is possiblethat one or more of the input devices can also be considered toconstitute one or more of the sensors (and vice-versa). Additionally,although in the present embodiment the input devices 210 are shown to bedistinct from the output devices 208, it should be recognized that insome embodiments one or more devices serve both as input device(s) andoutput device(s). In particular, in the present embodiment in which thedevice 102 includes the touch screen display 110, the touch screendisplay can be considered to constitute both a visual output device anda mechanical input device (by contrast, the keys or buttons 112 aremerely mechanical input devices).

The memory portion 206 of the internal components 200 can encompass oneor more memory devices of any of a variety of forms (e.g., read-onlymemory, random access memory, static random access memory, dynamicrandom access memory, etc.), and can be used by the processor 204 tostore and retrieve data. In some embodiments, the memory portion 206 canbe integrated with the processor 204 in a single device (e.g., aprocessing device including memory or processor-in-memory (PIM)), albeitsuch a single device will still typically have distinctportions/sections that perform the different processing and memoryfunctions and that can be considered separate devices. In some alternateembodiments, the memory portion 206 of the device 102 can besupplemented or replaced by other memory portion(s) located elsewhereapart from the mobile device and, in such embodiments, the mobile devicecan be in communication with or access such other memory device(s) byway of any of various communications techniques, for example, wirelesscommunications afforded by the wireless transceivers 202, or connectionsvia the component interface 212.

The data that is stored by the memory portion 206 can include, but neednot be limited to, operating systems, programs (applications), modules,and informational data. Each operating system includes executable codethat controls basic functions of the device 102, such as interactionamong the various components included among the internal components 200,communication with external devices via the wireless transceivers 202and/or the component interface 212, and storage and retrieval ofprograms and data, to and from the memory portion 206. As for programs,each program includes executable code that utilizes an operating systemto provide more specific functionality, such as file system service andhandling of protected and unprotected data stored in the memory portion206. Such programs can include, among other things, programming forenabling the device 102 to perform a process such as the process forspeech recognition shown in FIG. 3 and discussed further below. Finally,with respect to informational data, this is non-executable code orinformation that can be referenced and/or manipulated by an operatingsystem or program for performing functions of the device 102.

FIG. 3 shows example modules of a processor 300 of the electronic deviceof FIG. 1, in accordance with an embodiment of the disclosure. Theprocessor 300 may be an example of the processor 204 shown in FIG. 2. Asshown in FIG. 3, the processor 300 includes an enrollment phraserecorder 302, an analyzer 304, and an accept/reject flag setting unit306.

During the enrollment process, the device 102 prompts a user to speakthe trigger phrase into the microphone 108 (FIG. 1). The enrollmentphrase recorder 302 records the spoken trigger phrase so that therecorded audio signal can be analyzed by the analyzer 304. For eachframe of the recorded audio signal, the analyzer 304 measures thechannel energies and background noise energies of the recorded audiosignal. Based on the measured channel energies and background noiseenergies in the spectral domain, the analyzer 304 sets the VoiceActivity Detection (VAD) flag for the frame. If the analyzer 304determines that the frame being analyzed contains voice activity, theanalyzer 304 sets the VAD flag to 1. Otherwise, the analyzer 304 setsthe VAD flag to 0.

Furthermore, the analyzer 304 analyzes various characteristics of thespoken trigger phrase in the recorded audio signal, and compares thecharacteristics with predetermined threshold values. Then, the analyzer304 outputs the results of the comparisons to the accept/reject flagsetting unit 306. The accept/reject flag setting unit 306 uses theresults of the comparisons from the analyzer 304 to set either an“Accept Enrollment” flag or an “Reject Enrollment” flag. If theaccept/reject flag setting unit 306 sets the “Reject Enrollment” flag,the device 102 may prompt the user to redo the enrollment recording.

In order to determine and set the threshold values, the characteristicsof valid instances of the trigger phrase are first identified. Thisanalysis may be performed offline. The characteristics being analyzedmay include background noise level of the instances of the triggerphrase, length of the trigger phrase (e.g., number of frames in theinstances of the trigger phrase having voice activity), number ofsegments in the instances of the trigger phrase having voice activity,the length (e.g., number of frames) in the shortest segment, and ameasure of noise variability of the background noise (e.g., car noiseexhibits low noise variability while babble noise exhibits high noisevariability). Based on the analysis, various threshold values may beassigned to each of the analyzed characteristics and stored in thememory 206 for use by the analyzer 304.

Referring to FIG. 4, a procedure 400 carried out by the electronicdevice 102 (FIG. 1) according to an embodiment will now be described. Inthe present embodiment, the trigger phrase is “Okay Google Now.” Inother embodiments, however, other trigger phrases may be used.

At step 402, the device 102 records an audio signal that includes aspoken trigger phrase. At step 404, the device 102 measures a backgroundnoise level in the audio signal. Next, at step 406, the device 102compares the measured background noise level to a threshold level. Thedevice 102 may determine whether the measured background noise level isgreater than the threshold level. If the measured background noise levelis determined to be greater than the threshold level (i.e., there is ahigh level of background noise present in the recorded audio signal),the device 102 determines that the spoken trigger phrase is notacceptable for trigger phrase model training. In the present embodiment,the threshold level may be about −50 dB.

Based on the result of the comparison from step 406, the device 102determines whether the spoken trigger phrase is acceptable for triggerphrase model training. If the measured background noise level isdetermined to be greater than the threshold level (YES of step 406), thedevice 102 rejects the spoken trigger phrase for trigger phrase modeltraining at step 408. Otherwise, the device 102 will check additionalcharacteristics of the spoken trigger phrase to determine whether thespoken trigger phrase is acceptable for trigger phrase model training atstep 410.

Referring to FIG. 5, a procedure 500 carried out by the electronicdevice 102 (FIG. 1) according to another embodiment will now bedescribed. In the present embodiment, the trigger phrase is “Okay GoogleNow.” In other embodiments, however, other trigger phrases may be used.

At step 502, the device 102 records an audio signal that includes aspoken trigger phrase. At step 504, the device 102 estimates the lengthof the spoken trigger phrase in the audio signal. In an embodiment, toestimate the length of the spoken trigger phrase, the device 102 maycount the number of frames in the audio signal having voice activity(i.e., VAD flag=1).

Next, at step 506, the device 102 compares the estimated length of thespoken trigger phrase to a lower trigger phrase length threshold todetermine whether the estimated length of the spoken trigger phrase isless than the lower trigger phrase length threshold (i.e., whether therecorded phrase is too short). If the estimated length of the spokentrigger phrase is less than the lower trigger phrase length threshold(YES of step 506), the device 102 rejects the spoken trigger phrase fortrigger phrase model training at step 508. Otherwise, the device 102will check additional characteristics of the spoken trigger phrase todetermine whether the spoken trigger phrase is acceptable for triggerphrase model training at step 510. In the present embodiment, the lowertrigger phrase length threshold may be about 70 frames, and each framemay be of 10 ms duration.

Referring to FIG. 6, a procedure 600 carried out by the electronicdevice 102 (FIG. 1) according to yet another embodiment will now bedescribed. In the present embodiment, the trigger phrase is “Okay GoogleNow.” In other embodiments, however, other trigger phrases may be used.

At step 602, the device 102 records an audio signal that includes aspoken trigger phrase. At step 604, the device 102 estimates the lengthof the spoken trigger phrase in the audio signal. In an embodiment, toestimate the length of the spoken trigger phrase, the device 102 maycount the number of frames in the audio signal having voice activity(i.e., VAD flag=1).

Next, at step 606, the device 102 compares the estimated length of thespoken trigger phrase to a higher trigger phrase length threshold todetermine whether the estimated length of the spoken trigger phrase isgreater than the higher trigger phrase length threshold (i.e., whetherthe recorded phrase is too long). If the estimated length of the spokentrigger phrase is greater than the higher trigger phrase lengththreshold (YES of step 606), the device 102 rejects the spoken triggerphrase for trigger phrase model training at step 608. Otherwise, thedevice 102 will check additional characteristics of the spoken triggerphrase to determine whether the spoken trigger phrase is acceptable fortrigger phrase model training at step 610. In the present embodiment,the higher trigger phrase length threshold may be about 180 frames.

Referring to FIG. 7, a procedure 700 carried out by the electronicdevice 102 (FIG. 1) according to still another embodiment will now bedescribed. In the present embodiment, the trigger phrase is “Okay GoogleNow.” In other embodiments, however, other trigger phrases may be used.

At step 702, the device 102 records an audio signal that includes aspoken trigger phrase. At step 704, the device 102 measures a number ofsegments in the audio signal having voice activity. A segment is definedhere as a sequence of contiguous frames.

Next, at step 706, the device 102 compares the measured number ofsegments to a threshold value. The device 102 may determine whether themeasured number of segments is greater than the threshold value. If themeasured number of segments is determined to be greater than thethreshold value (YES at step 706), the device 102 determines that thespoken trigger phrase is not acceptable for trigger phrase modeltraining and rejects the spoken trigger phrase at step 708. Otherwise,the device 102 will check additional characteristics of the spokentrigger phrase to determine whether the spoken trigger phrase isacceptable for trigger phrase model training at step 710.

The threshold value for the number of segments may be based on anoffline analysis of the trigger phrase. The analysis may take intoaccount the number of words or syllables in the trigger phrase. In thepresent embodiment, since the trigger phrase is “Okay Google Now” (i.e.,3 words), the threshold value is set to 3.

Referring to FIG. 8, a procedure 800 carried out by the electronicdevice 102 (FIG. 1) according to an embodiment will now be described. Inthe present embodiment, the trigger phrase is “Okay Google Now.” Inother embodiments, however, other trigger phrases may be used.

At step 802, the device 102 records an audio signal that includes aspoken trigger phrase. At step 804, the device 102 measures the lengthof the shortest segment in the audio signal having voice activity (i.e.,VAD flag=1). In an embodiment, to estimate the length of the shortestsegment with voice activity, the device 102 may count the number ofsegments in the audio signal and/or the number of frames in each segmentthat have voice activity.

Next, at step 806, the device 102 compares the measured length of theshortest segment to a threshold value. The device 102 may determinewhether the measured length of the shortest segment is less than thethreshold value (i.e., indicating the presence of a “pop” or “click”sound in the recorded audio signal). If the measured length of theshortest segment is determined to be less than the threshold value (YESof step 806), the device determines that the spoken trigger phrase isnot acceptable for trigger phrase model training and rejects the spokentrigger phrase at step 808. Otherwise, the device 102 will checkadditional characteristics of the spoken trigger phrase to determinewhether the spoken trigger phrase is acceptable for trigger phrase modeltraining at step 810. In the present embodiment, the threshold value forthe length of the shortest segment may be about 27 frames.

Referring to FIG. 9, a procedure 900 carried out by the electronicdevice 102 (FIG. 1) according to a further embodiment will now bedescribed. In the present embodiment, the trigger phrase is “Okay GoogleNow.” In other embodiments, however, other trigger phrases may be used.

At step 902, the device 102 records an audio signal that includes aspoken trigger phrase. The audio signal is made up of frames. At step904, the device 102 sets the frame number to 1 (i.e., the first frame isthe current frame) and sets COUNT (number of frames counted) to 0. Atstep 906, the device 102 calculates a measure of noise variability ofbackground noise for the current frame in the audio signal. Then at step908, the device 102 compares the measure of noise variability ofbackground noise for the current frame to a first threshold value.

To calculate the measure of noise variability of the background noise,the device 102 may use the following equation:

${{MNV} = {\frac{1}{{NC} \times {nb}}{\sum\limits_{k = 1}^{NC}{\sum\limits_{l = 1}^{nb}\frac{\left( {{{D\_ smooth}\left( {k,l} \right)} - {{D\_ smooth}{\_ low}\left( {k,l} \right)}} \right)}{\left( {{{D\_ smooth}{\_ high}\left( {k,l} \right)} - {{D\_ smooth}{\_ low}\left( {k,l} \right)}} \right)}}}}},$wherein MNV denotes the measure noise variability of the backgroundnoise of the audio signal, NC denotes a number of channels of the audiosignal, nb+1 denotes a number of contiguous noise frames of the audiosignal, k denotes a channel index, l denotes a look-back index,D_smooth(k, l) denotes a smoothed maximum dB difference of smoothedchannel noise, D_smooth_high(k, l) denotes a high boundary point thatrepresents noise exhibiting high variability, and D_smooth low (k, l)denotes a low boundary point that represents noise exhibiting lowvariability. In an embodiment, MNV may be bounded between 0 and 1. Themeasure of noise variability (MNV) typically ranges from 0 to 1 with lowvalues corresponding to low variability noise signals (e.g., car noise)and high values corresponding to high variability noise signals (e.g.,babble noise). For a more detailed discussion of the measure of noisevariability, see U.S. patent application Ser. No. 13/950,980 entitled“METHOD AND APPARATUS FOR ESTIMATING VARIABILITY OF BACKGROUND NOISE FORNOISE SUPPRESSION” filed on Jul. 25, 2013, which is hereby incorporatedherein by reference in its entirety.

Next, if the device 102 determines that the measure of noise variabilityfor the current frame is greater than the first threshold value (YES ofstep 908), then the device 102 increments COUNT at step 910. Once thedevice 102 increments COUNT, the device 102 determines whether the lastframe of the audio signal has been reached at step 912. If the device102 determines that the measure of noise variability for the currentframe is equal to or less than the first threshold value (NO of step908), the device 102 does not increment COUNT but proceeds directly tostep 912 to determine whether the last frame of the audio signal hasbeen reached. If the last frame of the audio signal has been reached(YES of step 912), the device 102 proceeds to step 914. On the otherhand, if the last frame has not been reached (NO of step 912), thedevice 102 then increments the frame number at step 920 (i.e., thecurrent frame is now the incremented frame) and returns to step 906.

At step 914, the device 102 compares the number of frames in the audiosignal for which the measure of noise variability of the backgroundnoise is higher than the first threshold value (e.g., background noiseexhibits high noise variability, such as babble noise) to a secondthreshold value (i.e., whether COUNT≥second threshold value). If thecounted number of frames is greater than or equal to the secondthreshold value (YES of step 914), the device 102 determines that thespoken trigger phrase is not acceptable for trigger phrase modeltraining and rejects the spoken trigger phrase at step 916. Otherwise,the device 102 will check additional characteristics of the spokentrigger phrase to determine whether the spoken trigger phrase isacceptable for trigger phrase model training at step 918. In the presentembodiment, the first threshold value may be about 0.7, and the secondthreshold value may be about 20. Furthermore, in another embodiment, thedevice 102 may enable counting of the number of frames only when the VADflag is set to 0.

In the various embodiments, the threshold values are dependent on theparticular characteristics of a trigger phrase. Therefore, the thresholdvalues may vary based on the number of words, syllables, or phonemes ina trigger phrase. Accordingly, the threshold values presented in thecurrent disclosure are exemplary only and should not be construed aslimiting. Furthermore, the method and apparatus disclosed herein may beadapted and employed for enrollment recordings of various triggerphrases.

All signal/noise dB values presented in the current disclosure aredB-FS, i.e. dB, (full-scale). This means that when the signal isfull-scale (e.g. +/−32768 for 16-bit representation of signal samples),the corresponding reference dB level is 0.

EXAMPLE 1

A method comprising: recording an audio signal including a spokentrigger phrase; measuring a background noise level in the audio signal;comparing the measured background noise level to a threshold level;determining, based on the comparing step, whether the spoken triggerphrase is acceptable for trigger phrase model training; and if thespoken trigger phrase is determined not to be acceptable for triggerphrase model training, rejecting the spoken trigger phrase for triggerphrase model training.

EXAMPLE 2

The method of example 1, wherein the determining step comprises:determining whether the measured background noise level is greater thanthe threshold level; and if the measured background noise level isdetermined to be greater than the threshold level, determining that thespoken trigger phrase is not acceptable for trigger phrase modeltraining.

EXAMPLE 3

The method of example 2, wherein the threshold level is −50 dB.

EXAMPLE 4

A method comprising: recording an audio signal including a spokentrigger phrase; estimating the length of the spoken trigger phrase inthe audio signal; determining whether the estimated length of the spokentrigger phrase is less than a lower trigger phrase length threshold; andif the estimated length of the spoken trigger phrase is less than thelower trigger phrase length threshold, rejecting the spoken triggerphrase for trigger phrase model training.

EXAMPLE 5

The method of example 4, wherein the estimating of the length of thespoken trigger phrase comprises counting the number of frames in theaudio signal having voice activity.

EXAMPLE 6

The method of example 4, wherein the lower trigger phrase lengththreshold is 70 frames.

EXAMPLE 7

A method comprising: recording an audio signal including a spokentrigger phrase; estimating the length of the spoken trigger phrase inthe audio signal; and determining whether the estimated length of thespoken trigger phrase is greater than a higher trigger phrase lengththreshold; and if the estimated length of the spoken trigger phrase isgreater than the higher trigger phrase length threshold, rejecting thespoken trigger phrase for trigger phrase model training.

EXAMPLE 8

The method of example 7, wherein the estimating of the length of thespoken trigger phrase comprises counting the number of frames havingvoice activity in the audio signal.

EXAMPLE 9

The method of example 7, wherein the higher trigger phrase lengththreshold is 180 frames.

EXAMPLE 10

A method comprising: recording an audio signal including a spokentrigger phrase; measuring a number of segments in the audio signalhaving voice activity; comparing the measured number of segments to athreshold value; determining, based on the comparing step, whether thespoken trigger phrase is acceptable for trigger phrase model training;and if the spoken trigger phrase is determined not to be acceptable fortrigger phrase model training, rejecting the spoken trigger phrase fortrigger phrase model training.

EXAMPLE 11

The method of example 10, wherein the determining step comprises:determining whether the measured number of segments is greater than thethreshold value; and if the measured number of segments is determined tobe greater than the threshold value, determining that the spoken triggerphrase is not acceptable for trigger phrase model training.

EXAMPLE 12

The method of example 10, wherein the threshold value is based on anoffline analysis of the trigger phrase.

EXAMPLE 13

A method comprising: recording an audio signal including a spokentrigger phrase; measuring the length of the shortest segment in theaudio signal having voice activity; comparing the measured length of theshortest segment to a threshold value; determining, based on thecomparing step, whether the spoken trigger phrase is acceptable fortrigger phrase model training; and if the spoken trigger phrase isdetermined not to be acceptable for trigger phrase model training,rejecting the spoken trigger phrase for trigger phrase model training.

EXAMPLE 14

The method of example 13, wherein the determining step comprises:determining whether the measured length of the shortest segment is lessthan the threshold value; and if the measured length of the shortestsegment is determined to be less than the threshold value, determiningthat the spoken trigger phrase is not acceptable for trigger phrasemodel training.

EXAMPLE 15

The method of example 14, wherein the threshold value is 27 frames.

Example 16

A method comprising: recording an audio signal including a spokentrigger phrase; calculating a measure of noise variability of backgroundnoise for each frame in the audio signal; comparing the measure of noisevariability of background noise for each frame to a first thresholdvalue; counting the number of frames in the audio signal for which themeasure of noise variability of the background noise is higher than thefirst threshold value; comparing the counted number of frames to asecond threshold value; determining, based on the counted number offrames, whether the spoken trigger phrase is acceptable for triggerphrase model training; and if the spoken trigger phrase is determinednot to be acceptable for trigger phrase model training, rejecting thespoken trigger phrase for trigger phrase model training.

EXAMPLE 17

The method of example 16, wherein the determining step comprises:determining whether the counted number of frames is equal to or greaterthan the second threshold value; and if the counted number of frames isdetermined to be equal to or greater than the second threshold value,determining that the spoken trigger phrase is not acceptable for triggerphrase model training.

EXAMPLE 18

The method of example 17, wherein the first threshold value is 0.7 andthe second threshold value is 20.

EXAMPLE 19

The method of example 16, wherein the measure of noise variability ofthe background noise is calculated using the following equation:

${{MNV} = {\frac{1}{{NC} \times {nb}}{\sum\limits_{k = 1}^{NC}{\sum\limits_{l = 1}^{nb}\frac{\left( {{{D\_ smooth}\left( {k,l} \right)} - {{D\_ smooth}{\_ low}\left( {k,l} \right)}} \right)}{\left( {{{D\_ smooth}{\_ high}\left( {k,l} \right)} - {{D\_ smooth}{\_ low}\left( {k,l} \right)}} \right)}}}}},$wherein MNV denotes the measure noise variability of the backgroundnoise in the audio signal, NC denotes a number of channels in the audiosignal, nb+1 denotes a number of contiguous noise frames in the audiosignal, k denotes a channel index, l denotes a look-back index,D_smooth(k, l) denotes a smoothed maximum dB difference of smoothedchannel noise, D_smooth_high(k, l) denotes a high boundary point thatrepresents noise exhibiting high variability, and D_smooth_low (k, l)denotes a low boundary point that represents noise exhibiting lowvariability.

EXAMPLE 20

A device comprising: a microphone that receives an audio signal thatincludes a spoken trigger phrase; a processor that is electricallycoupled to the microphone, wherein the processor: measurescharacteristics of the audio signal; determines, based on the measuredcharacteristics, whether the spoken trigger phrase is acceptable fortrigger phrase model training; and if the spoken trigger phrase isdetermined not to be acceptable for trigger phrase model training,rejects the trigger phrase for trigger phrase model training.

EXAMPLE 21

The device of example 20, wherein the processor further: measures abackground noise level in the audio signal; compares the measuredbackground noise level to a threshold level; determines whether themeasured background noise level is greater than the threshold level; andif the measured background noise is determined to be greater than thethreshold level, determines that the spoken trigger phrase is notacceptable for trigger phrase model training.

EXAMPLE 22

The device of example 20, wherein the processor further: estimates thelength of the spoken trigger phrase in the audio signal; determineswhether estimated length of the spoken trigger phrase is less than alower trigger phrase length threshold; and if the estimated length ofthe spoken trigger phrase is less than the lower trigger phrase lengththreshold, determines that the spoken trigger phrase is not acceptablefor trigger phrase model training.

EXAMPLE 23

The device of example 20, wherein the processor further: estimates thelength of the spoken trigger phrase in the audio signal; determineswhether the estimated length of the spoken trigger phrase is greaterthan a higher trigger phrase length threshold; and if the estimatedlength of the spoken trigger phrase is greater than the higher triggerphrase length threshold, determines that the spoken trigger phrase isnot acceptable for trigger phrase model training.

EXAMPLE 24

The device of example 20, wherein the processor further: measures anumber of segments in the audio signal having voice activity; comparesthe number of segments measured to a threshold value; determines whetherthe measured number of segments is greater than the threshold value; andif the measured number of segments is determined to be greater than thethreshold value, determines that the spoken trigger phrase is notacceptable for trigger phrase model training.

EXAMPLE 25

The device of example 20, wherein the processor further: measures thelength of the shortest segment in the audio signal having voiceactivity; compares the measured length of the shortest segment to athreshold value; determining whether the measured length of the shortestsegment is less than a threshold value; and if the measured length ofthe shortest segment is less than the threshold value, determines thatthe spoken trigger phrase is not acceptable for trigger phrase modeltraining.

EXAMPLE 26

The device of example 20, wherein the processor further: calculates ameasure of noise variability of background noise for each frame in theaudio signal; compares the measure of noise variability of backgroundnoise for each frame to a first threshold value; counts the number offrames in the audio signal for which the measure of noise variability ofthe background noise is higher than the first threshold value; comparesthe counted number of frames to a second threshold value; determineswhether the counted number of frames is equal to or greater than thesecond threshold value; and if the counted number of frames is equal toor greater than the second threshold value, determines that the spokentrigger phrase is not acceptable for trigger phrase model training.

It can be seen from the foregoing that a method for apparatus forevaluating trigger phrase enrollment for trigger phrase training hasbeen provided. In view of the many possible embodiments to which theprinciples of the present discussion may be applied, it should berecognized that the embodiments described herein with respect to thedrawing figures are meant to be illustrative only and should not betaken as limiting the scope of the claims. Therefore, the techniques asdescribed herein contemplate all such embodiments as may come within thescope of the following claims and equivalents thereof.

What is claimed is:
 1. A computer-implemented method comprising: duringa trigger phrase enrollment process: receiving, at a speechrecognition-enabled electronic device, a first audio signalcorresponding to a user of the speech recognition-enabled electronicdevice speaking a trigger phrase, the first audio signal comprising afirst number of frames having a measure of noise variability ofbackground noise exceeding a noise variability threshold; when a countof the first number of frames in the first audio signal satisfies aframe number threshold, prompting, by the speech recognition-enabledelectronic device, the user to speak the trigger phrase again;receiving, by the speech recognition-enabled electronic device, a secondaudio signal corresponding to the user speaking the trigger phraseagain, the second audio signal comprising a second number of frameshaving the measure of noise variability of background noise exceedingthe noise variability threshold; and when a count of the second numberof frames in the second audio signal dissatisfies the frame numberthreshold, training, by the speech recognition-enabled electronicdevice, a trigger phrase model with the second audio signalcorresponding to the user speaking the trigger phrase again; and afterthe trigger phrase enrollment process: receiving, at the speechrecognition-enabled electronic device and while the speechrecognition-enabled electronic device is in a sleep mode, a third audiosignal including an utterance of the trigger phrase spoken by the user;and detecting, by the speech recognition-enabled electronic device andusing the trigger phrase model trained during the trigger phraseenrollment process, the utterance of the trigger phrase in the thirdaudio signal, the trigger phrase when detected in the third audio signalcausing the speech recognition-enabled electronic device to wake fromthe sleep mode, the sleep mode comprising a power-saving mode ofoperation in which one or more parts of the speech recognition-enabledelectronic device are in a low-power state or powered off.
 2. Thecomputer-implemented method of claim 1, wherein the count of the firstnumber of frames satisfies the frame number threshold when the firstnumber of frames is greater than or equal to the frame number threshold,and wherein the count of the second number of frames dissatisfies theframe number threshold when the second number of frames is less than theframe number threshold.
 3. The computer-implemented method of claim 1,further comprising: determining, by the speech recognition-enabledelectronic device, the measure of noise variability of the backgroundnoise for each frame in the received first audio signal; comparing, bythe speech recognition-enabled electronic device, the determined measureof noise variability of the background noise to the noise variabilitythreshold; and incrementing, by the speech recognition-enabledelectronic device, a counter in response to determining the determinedmeasure of noise variability of the background noise is greater than thenoise variability threshold.
 4. The computer-implemented method of claim1, further comprising: determining, by the speech recognition-enabledelectronic device, the measure of noise variability of the backgroundnoise for each frame in the received second audio signal; comparing, bythe speech recognition-enabled electronic device, the determined measureof noise variability of the background noise to the noise variabilitythreshold; and incrementing, by the speech recognition-enabledelectronic device, a counter in response to determining the determinedmeasure of noise variability of the background noise is greater than thenoise variability threshold.
 5. The computer-implemented method of claim4, wherein determining the measure of the noise variability of thebackground noise for each frame in the received second audio signalcomprises: obtaining, by the speech recognition-enabled electronicdevice, a number of channels in the received second audio signal;obtaining, by the speech recognition-enabled electronic device, a numberof contiguous noise frames in the received second audio signal;determining, by the speech recognition-enabled electronic device, acurrent channel index associated with each of the number of channels inthe received second audio signal; obtaining, by the speechrecognition-enabled electronic device, a look-back index; obtaining, bythe speech recognition-enabled electronic device, a smoothed maximumdifference of smoothed channel noise; obtaining, by the speechrecognition-enabled electronic device, a high boundary pointrepresenting noise exhibiting high noise variability; and obtaining, bythe speech recognition-enabled electronic device, a low boundary pointrepresenting noise exhibiting low noise variability.
 6. Thecomputer-implemented method of claim 5, further comprising determining,by the speech recognition-enabled electronic device, the measure of thenoise variability of the background noise based on the number ofchannels, the number of contiguous noise frames, the current channelindex, the look-back index, the smoothed maximum difference of thesmoothed channel noise, the high boundary point, and the low boundarypoint, wherein the measure of the noise variability of the backgroundnoise is a value greater than 0and less than
 1. 7. Thecomputer-implemented method of claim 6, wherein the noise variabilitythreshold is greater than 0.7 and the frame number threshold is greaterthan
 20. 8. A system comprising: one or more computers and one or morestorage devices storing instructions that are operable, when executed bythe one or more computers, to cause the one or more computers to performoperations comprising: during a trigger phrase enrollment process:receiving a first audio signal corresponding to a user of a speechrecognition-enable electronic device speaking a trigger phrase into thespeech recognition-enabled electronic device, the first audio signalcomprising a first number of frames having a measure of noisevariability of background noise exceeding a noise variability threshold;when a count of the first number of frames in the first audio signalsatisfies a frame number threshold, prompting the user to speak thetrigger phrase into the speech recognition- enabled electronic deviceagain; receiving a second audio signal corresponding to the userspeaking the trigger phrase again, the second audio signal comprising asecond number of frames having the measure of noise variability ofbackground noise exceeding the noise variability threshold; and when acount of the second number of frames in the second audio signaldissatisfies the frame number threshold, training a trigger phrase modelwith the second audio signal corresponding to the user speaking thetrigger phrase again; and after the trigger phrase enrollment process:receiving, while the speech recognition-enabled electronic device is ina sleep mode, a third audio signal including an utterance of the triggerphrase spoken by the user; and detecting, using the trigger phrase modeltrained during the trigger phrase enrollment process, the utterance ofthe trigger phrase in the third audio signal, the trigger phrase whendetected in the third audio signal causing the speechrecognition-enabled electronic device to wake from the sleep mode, thesleep mode comprising a power-saving mode of operation in which one ormore parts of the speech recognition-enabled electronic device are in alow-power state or powered off.
 9. The system of claim 8, wherein thecount of the first number of frames satisfies the frame number thresholdwhen the first number of frames is greater than or equal to the framenumber threshold, and wherein the count of the second number of framesdissatisfies the frame number threshold when the second number of framesis less than the frame number threshold.
 10. The system of claim 8,wherein the operations further comprise: determining the measure ofnoise variability of the background noise for each frame in the receivedfirst audio signal; comparing the determined measure of noisevariability of the background noise to the noise variability threshold;and incrementing a counter in response to determining the determinedmeasure of noise variability of the background noise is greater than thenoise variability threshold.
 11. The system of claim 8, wherein theoperations further comprise: determining the measure of noisevariability of the background noise for each frame in the receivedsecond audio signal; comparing the determined measure of noisevariability of the background noise to the noise variability threshold;and incrementing a counter in response to determining the determinedmeasure of noise variability of the background noise is greater than thenoise variability threshold.
 12. The system of claim 11, whereindetermining the measure of the noise variability of the background noisefor each frame in the received second audio signal comprises: obtaininga number of channels in the received second audio signal; obtaining anumber of contiguous noise frames in the received second audio signal;determining a current channel index associated with each of the numberof channels in the received second audio signal; obtaining a look-backindex; obtaining a smoothed maximum difference of smoothed channelnoise; obtaining a high boundary point representing noise exhibitinghigh noise variability; and obtaining a low boundary point representingnoise exhibiting low noise variability.
 13. The system of claim 12,wherein the operations further comprise determining the measure of thenoise variability of the background noise based on the number ofchannels, the number of contiguous noise frames, the current channelindex, the look-back index, the smoothed maximum difference of thesmoothed channel noise, the high boundary point, and the low boundarypoint, wherein the measure of the noise variability of the backgroundnoise is a value greater than 0 and less than
 1. 14. The system of claim13, wherein the noise variability threshold is greater than 0.7 and theframe number threshold is greater than
 20. 15. A non-transitorycomputer-readable medium storing software comprising instructionsexecutable by one or more computers which, upon such execution, causethe one or more computers to perform operations comprising: during atrigger phrase enrollment process: receiving a first audio signalcorresponding to a user of a speech recognition-enabled electronicdevice speaking a trigger phrase into the speech recognition-enabledelectronic device, the first audio signal comprising a first number offrames having a measure of noise variability of background noiseexceeding a noise variability threshold; when a count of the firstnumber of frames in the first audio signal satisfies a frame numberthreshold, prompting the user to speak the trigger phrase into thespeech recognition-enabled electronic device again; receiving a secondaudio signal corresponding to the user speaking the trigger phraseagain, the second audio signal comprising a second number of frameshaving the measure of noise variability of background noise exceedingthe noise variability threshold; and when a count of the second numberof frames in the second audio signal dissatisfies the frame numberthreshold, training a trigger phrase model with the second audio signalcorresponding to the user speaking the trigger phrase again; and afterthe trigger phrase enrollment process: receiving, while the speechrecognition-enabled electronic device is in a sleep mode, a third audiosignal including an utterance of the trigger phrase spoken by the user;and detecting, using the trigger phrase model trained during the triggerphrase enrollment process, the utterance of the trigger phrase in thethird audio signal, the trigger phrase when detected in the third audiosignal causing the speech recognition-enabled electronic device to wakefrom the sleep mode, the sleep mode comprising a power-saving mode ofoperation in which one or more parts of the speech recognition-enabledelectronic device are in a low-power state or powered off.
 16. Thecomputer-readable medium of claim 15, wherein the count of the firstnumber of frames satisfies the frame number threshold when the firstnumber of frames is greater than or equal to the frame number threshold,and wherein the count of the second number of frames dissatisfies theframe number threshold when the second number of frames is less than theframe number threshold.
 17. The computer-readable medium of claim 15,wherein the operations further comprise: determining the measure ofnoise variability of the background noise for each frame in the receivedfirst audio signal; comparing the determined measure of noisevariability of the background noise to the noise variability threshold;and incrementing a counter in response to determining the determinedmeasure of noise variability of the background noise is greater than thenoise variability threshold.
 18. The computer-readable medium of claim15, wherein the operations further comprise: determining the measure ofnoise variability of the background noise for each frame in the receivedsecond audio signal; comparing the determined measure of noisevariability of the background noise to the noise variability threshold;and incrementing a counter in response to determining the determinedmeasure of noise variability of the background noise is greater than thenoise variability threshold.
 19. The computer-readable medium of claim18, wherein determining the measure of the noise variability of thebackground noise for each frame in the received second audio signalcomprises: obtaining a number of channels in the received second audiosignal; obtaining a number of contiguous noise frames in the receivedsecond audio signal; determining a current channel index associated witheach of the number of channels in the received second audio signal;obtaining a look-back index; obtaining a smoothed maximum difference ofsmoothed channel noise; obtaining a high boundary point representingnoise exhibiting high noise variability; and obtaining a low boundarypoint representing noise exhibiting low noise variability.
 20. Thecomputer-readable medium of claim 19, wherein the operations furthercomprise determining the measure of the noise variability of thebackground noise based on the number of channels, the number ofcontiguous noise frames, the current channel index, the look-back index,the smoothed maximum difference of the smoothed channel noise, the highboundary point, and the low boundary point, wherein the measure of thenoise variability of the background noise is a value greater than 0 andless than 1.